This series of files compile all analyses done during Chapter 2.

All analyses have been done with R 4.0.2.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it


1. Maps

1.1. General map

Stations considered for this Chapter:

1.2. Parameters maps

Maps of functional traits density:

Body: non-calcified tissue

Body: calcareous

Body: calcium carbonate

Body: amorphous calcium carbonate

Body: aragonite

Body: calcite

Body: high magnesium calcite

Body: chitinous

Size: small

Size: medium

Size: large

Food: filter feeders

Food: surface deposit feeders

Food: subsurface deposit feeders

Food: grazers

Food: predators

Food: scavengers

Food: parasites

Mobility: sessile

Mobility: limited

Mobility: mobile

Lifestyle: fixed

Lifestyle: tubicolous

Lifestyle: burrower

Lifestyle: crawler

Lifestyle: swimmer

2. Exploration plots

2.1. Rank-Frequency diagrams

We drew Rank-Frequency diagrams to study the structure of communities when considering frequencies of taxa.

2.2. Abundance-Biomass curves

We drew Abundance-Biomass curves to study the structure of communities when considering density and biomass together.

3. Indicators of ecosystem status

This section tests different indicators to reflect the environmental status in Baie des Sept Îles. We have considered classic methods, such as community characteristics, with functional diversity indices and other techniques. We will look at their results critically to see which could be the best for which situation.

Indices have been grouped based on Salas et al. 2006 (Ocean and Coastal Management).

3.1. Based on species abundance and biomass

3.1.1. Total density & biomass

We calculated a basic community characteristic, the total density of individuals, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.

When we considered the data without a distinction by station, global total density is 10,915 individuals.grab-1.

3.1.2. Total biomass

We calculated a basic community characteristic, the total biomass of individuals, to see if patterns could be detected in the study area.

When we considered the data without a distinction by station, global total biomass is 936.6919 gWM.grab-1.

3.1.3. W statistic

This indicator is based on abundance and biomass ranked values, as presented by Warwick (1986) and Clarke (1990). In addition to Abundance-Biomass Curves (see above), it allows to present a disturbed state thanks to a certain structure of the community.

The W statistic is continuous between -1 and 1, and is calculated using this equation:

\[ W = \frac{\sum_{i = 1}^{S}(B_{i} - A_{i})}{50(S - 1)} \]

  • \(B_{i}\) is the biomass of a species
  • \(A_{i}\) is the abundance of a species
  • \(S\) is the specific richness
  • \(i\) is a species rank

When we considered the data without a distinction by station, global W statistic is 0.1035.

3.2. Based on diversity values

3.2.1. Specific richness

We calculated a basic community characteristic, the specific richness, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.

ASSUMPTION: A higher richness indicates a high status without perturbation.

When we considered the data without a distinction by station, the global specific richness is 132.

3.2.2. Shannon index

We calculated basic a community characteristic, the Shannon index, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.

ASSUMPTION: A higher index indicates a high status without perturbation.

When we considered the data without a distinction by station, the global Shannon index is 3.251545.

3.2.3. Margalef index

We calculated a basic community characteristics, the Margalef index, to see if patterns could be detected in the study area.

ASSUMPTION: A higher index indicates a high status without perturbation. (To check)

When we considered the data without a distinction by station, the global Margalef index is 14.09715.

3.2.4. Simpson index

We calculated a basic community characteristics, the Simpson index, to see if patterns could be detected in the study area.

When we considered the data without a distinction by station, the global Simpson index is 0.9266169.

3.2.5. Pielou evenness

We calculated a basic community characteristics, the Pielou evenness, to see if patterns could be detected in the study area. The same calculation as for Chapter 1 has been performed for the considered stations.

When we considered the data without a distinction by station, the global Pielou evenness is 0.6659178.

3.2.6. Taxonomic diversity

We calculated a basic community characteristic, the taxonomic diversity, to see if patterns could be detected in the study area. The same calculations as for Chapter 1 have been performed for the considered stations.

When we considered the data without a distinction by station, the global taxonomic diversity is 74.16541.

3.3. Based on ecological strategies

3.3.1. Functional diversity

We studied functional diversity based on five biological traits and 26 modalities:

  • body composition: non calcified tissue, calcareous, calcareous calcium carbonate, calcareous amorphous calcium carbonate, calcareous aragonite, calcareous calcite, calcareous high magnesium calcite, chitinous
  • body size: small, medium, large
  • food diet: filter, surface deposit, subsurface deposit, predator, scavenger, grazer, parasite
  • mobility: sessile, limited, mobile
  • lifestyle: fixed, tubicolous, burrower, crawler, swimmer

Species were assigned a value for each modality using a scale varying from 0 (absence of the modality) to 1 (presence). All where the sum of the values for every modality of a trait equals 1. This allowed to calculate functional richness, evenness and divergence according to Laliberté & Legendre (2010).

For some reason, R is not able to calculate a global value…

3.3.2. Benthic opportunistic polychaete/amphipod ratio (BOPA)

BOPA is an index that uses a relative abundance ratio of species in a community to infer a state of perturbation. Ratios with many species have been tested, and opportunistic polychaetes and amphipods have been selected to be the most pertinent (originally to detect effects of an oil-spill on soft-bottom communities, e.g. from the Sea Empress or the Amoco Cadiz). It has been updated from its original form in 2000.

BOPA is continuous between 0 and \(log_{10}(2)\) (~ 0.3), and is calculated using this equation:

\[ BOPA = \left( \frac{f_{P}}{f_{A} + 1} + 1 \right) \]

  • \(f_{P}\) is the relative frequency of opportunistic polychaetes (abundance / total density)
  • \(f_{A}\) is the relative frequency of amphipods (abundance / total density)

We considered ecological groups GIII to GV for polychaetes and GI for amphipods (without Jassa genera, see AMBI section below).

ASSUMPTION: Dominance of amphipods characterizes pristine ecosystems, while a dominance of opportunistic polychaetes indicates a perturbed state.

These are the polychaetes and amphipods present in our species list (including the confidence score used during group classification).

taxon_name group source confidence_score
cossura_longocirrata IV AMBI list 3
eteone_sp III AMBI list 2
hediste_diversicolor III AMBI list 3
praxillella_praetermissa III AMBI list 3
taxon_name group source confidence_score
ameroculodes_edwardsi I AMBI list 3
ampelisca_vadorum I AMBI list 3
byblis_gaimardii I AMBI list 3
lysianassidae_spp I AMBI list 2
maera_danae I AMBI list (Maera sp.) 2
phoxocephalus_holbolli I AMBI list 3
pontoporeia_femorata I AMBI list 3
quasimelita_formosa I AMBI list (MELITIDAE) 2
quasimelita_quadrispinosa I AMBI list 3

When we considered the data without a distinction by station, the global BOPA index is 0.003443646.

3.3.3. BenthoVal index

This index is a work-in-progress by the team of Céline Labrune at IFREMER. This pressure score still needs to be enhanced so that more human activities are included and the score is better defined.

3.4. Based on characteristic species

3.4.1. AZTI Marine Biotic Index (AMBI)

AMBI (initially named the biotic coefficient) is an ecological index that is used to detect a perturbation in an ecosystem based on the composition of the communities (Borja et al., 2000). This perturbation is linked with an organic matter increase, according to Pearson and Rosenberg (1978) model.

To compute the index, species are classed into five groups in relation to their tolerance to this perturbation:

  • group I (GI): vulnerable species
  • group II (GII): indifferent species
  • group III (GIII): tolerant species
  • group IV (GIV): first-order opportunistics
  • group V (GV): second-order opportunistics

These groups are based on expert opinion on the physiology of species and experimental studies, but the attribution of a species to a group can be somewhat arbitrary (e.g. based on related phyla information) so it needs to be interpretated carefully.

AMBI is continuous between 0 to 6 (7 when the habitat is azoic, thus considered extremely disturbed), and is calculated using this equation in a dedicated software:

\[ AMBI = \frac{\sum_{i}^{GI-V} w_{i} . P_{i}}{100} \]

  • \(P_{i}\) is the proportion of each group (percentage of the total density of species)
  • \(w_{i}\) is the weighting parameter of each group (respectively 0, 1.5, 3, 4.5 and 6)
  • \(i\) is the ecological group

ASSUMPTION: Sensitive species are only present in pristine ecosystems, while the dominance of opportunists indicates a perturbed state.

Here is the classification of the sampled taxa in ecological groups, adapted from Borja et al. (2000):

taxon_name group source confidence_score
aceroides_aceroides_latipes II AMBI list 3
akanthophoreus_gracilis I AMBI list 3
ameritella_agilis II AMBI list 3
ameroculodes_edwardsi I AMBI list 3
ampelisca_vadorum I AMBI list 3
amphipoda not_assigned 0
anonyx_lilljeborgi II AMBI list 3
anthozoa II AMBI list 1
arcteobia_anticostiensis II AMBI list (POLYNOIDAE) 2
arrhoges_occidentalis I AMBI list (Aporrhais sp.) 2
astarte_sp I AMBI list 2
axinopsida_orbiculata III AMBI list 3
axiothella_catenata I AMBI list (Axiothella sp.) 2
bathymedon_longimanus II AMBI list 3
bathymedon_obtusifrons II AMBI list 3
bipalponephtys_neotena II AMBI list 3
boreochiton_ruber I AMBI list 3
brachydiastylis_sp II AMBI list 2
byblis_gaimardii I AMBI list 3
cancer_irroratus II Gittenberg & Van Loon, 2013 (Cancer pagurus) 1
caprella_septentrionalis II AMBI list 3
chaetodermatida not_assigned 0
chionoecetes_opilio I AMBI list 3
chlamys_islandica I AMBI list 3
chone_sp II AMBI list 2
ciliatocardium_ciliatum I AMBI list 3
cirripedia II AMBI list 3
cistenides_granulata II AMBI list 3
cossura_longocirrata IV AMBI list 3
crassicorophium_bonellii III AMBI list 3
crenella_decussata I AMBI list 3
cumacea I AMBI list 3
cyclocardia_borealis I AMBI list (Cyclocardia thouarsii) 2
cyrtodaria_siliqua I Gilkinson et al., 2005 2
diastylis_rathkei III AMBI list 3
diastylis_sculpta II AMBI list 3
diastylis_sp I AMBI list 1
echinarachnius_parma I AMBI list (ECHINOIDEA) 2
edotia_montosa II AMBI list 3
ennucula_tenuis II AMBI list 3
eteone_sp III AMBI list 2
euchone_sp II AMBI list 2
eudorella_emarginata II AMBI list 3
eudorellopsis_integra II Tillin & Tyler-Walters, 2014 (group of Bathyporeia elegans & Eudorellopsis deformis) 2
euspira_pallida II AMBI list 3
glycera_capitata II AMBI list 3
glycera_sp II AMBI list 2
goniada_maculata II AMBI list 3
guernea_prinassus_nordenskioldi III de la Ossa Carretero et al., 2011 (Dexamene spinosa) 1
halacaridae_spp I AMBI list 2
haminoea_solitaria II AMBI list 3
hardametopa_carinata II AMBI list (STENOTHOIDAE) 1
harmothoe_sp II AMBI list 2
harpacticoida not_assigned 0
hediste_diversicolor III AMBI list 3
heteranomia_squamula I AMBI list 3
hiatella_arctica I AMBI list 3
holothuroidea I AMBI list 3
idotea_phosphorea II AMBI list (Idotea sp.) 2
ischyroceridae_spp II AMBI list (Ischyrocerus anguipes) 2
ischyrocerus_anguipes II AMBI list 3
isopoda not_assigned 0
lacuna_vincta II AMBI list 3
lamprops_fuscatus I AMBI list 3
lamprops_quadriplicata I AMBI list 3
lepeta_caeca I AMBI list 3
leucon_leucon_nasicoides II AMBI list 3
littorina_littorea II AMBI list 3
lumbrineridae_spp II AMBI list 2
lysianassidae_spp I AMBI list 2
macoma_calcarea II AMBI list 3
maera_danae I AMBI list (Maera sp.) 2
maldane_sarsi II AMBI list 3
maldanidae_spp I AMBI list 2
monoculopsis_longicornis II AMBI list 3
muculus_musculus_discors I AMBI list 3
mytilus_sp III AMBI list 2
nematoda III AMBI list 1
nemertea III AMBI list 1
neoleanira_tetragona II AMBI list 3
nephtyidae_spp II AMBI list 2
nephtys_caeca II AMBI list 3
nephtys_incisa II AMBI list 3
nephtys_sp II AMBI list 2
nuculana_minuta I AMBI list 3
nymphonidae_spp not_assigned 0
oenopota_sp I AMBI list 2
oligochaeta V AMBI list 1
ophelia_limacina I AMBI list 3
opheliidae_spp I AMBI list (Ophelia limacina) 2
ophiopholis_aculeata II AMBI list 3
ophiura_robusta II AMBI list 3
orchomenella_minuta II AMBI list 3
ostracoda I Bodegart et al., 1997 ; Ruiz et al., 2005 ; Gooday et al., 2009 1
pagurus_pubescens II AMBI list 3
pagurus_sp II AMBI list 2
pandalus_montagui II AMBI list 3
parathyasira_equalis III AMBI list 3
parvicardium_pinnulatum I AMBI list 3
periploma_leanum II AMBI list (Periploma discus) 2
philine_lima II AMBI list 3
philomedes_sp II AMBI list 3
pholoe_longa II AMBI list (Pholoe sp.) 2
pholoe_sp II AMBI list 2
phoxocephalus_holbolli I AMBI list 3
polynoidae_spp II AMBI list (POLYNOIDAE) 2
pontogeneia_inermis II AMBI list (Pontogeneia rostrata) 2
pontoporeia_femorata I AMBI list 3
praxillella_praetermissa III AMBI list 3
propebela_turricula I AMBI list 3
protomedeia_fasciata II AMBI list 3
protomedeia_grandimana II AMBI list 3
puncturella_noachina I AMBI list 3
quasimelita_formosa I AMBI list (MELITIDAE) 2
quasimelita_quadrispinosa I AMBI list 3
retusa_obtusa II AMBI list 3
sabellidae_spp I AMBI list 2
scoletoma_fragilis II AMBI list 3
scoletoma_sp II AMBI list 2
scoloplos_sp I AMBI list 2
serripes_groenlandicus I AMBI list 3
sipuncula I AMBI list 1
solamen_glandula II AMBI list (Solamen columbianum) 2
solariella_sp I AMBI list 2
strongylocentrotus_sp I AMBI list (Strongylocentrotus droebachiensis) 3
tachyrhynchus_erosus I AMBI list (Turritella sp.) 2
thracia_septentrionalis I AMBI list 3
thyasira_gouldi I AMBI list 3
thyasira_sp II AMBI list 1
trichotropis_bicarinata II AMBI list (Euspira sp.) 2
turritellopsis_stimpsoni I AMBI list (Turritella sp.) 2
yoldia_myalis I AMBI list (Yoldia limatula) 2

When we considered the data without a distinction by station, the global AMBI index is 1.543.

3.4.2. Multivariate AMBI (M-AMBI)

M-AMBI is a complementary method to AMBI that is based on a multivariate ordination (factorial analysis and discriminant analysis) of the stations using the species richness, the Shannon index and the AMBI index (Muxika et al. 2007). This index needs to explicitely define reference conditions, corresponding to ‘bad’ and ‘high’ conditions.

These values are called ‘references’, but this vision can be biaised. We have calculated them using the 5 % and 95 % percentiles of the distribution. This is a recommendation by Nicolas Desroy, so that we do not detect an modification of quality status when there is a small perturbation (see work by Pearson & Rosenberg and the Intermediate Disturbance Hypothesis).

This calculation provided S = 5, H = 0.98, AMBI = 0.75 for ‘bad’ conditions and S = 21, H = 2.53, AMBI = 2.52 for ‘high’ conditions.

M-AMBI is continuous between 0 and 1, and is calculated using a dedicated software.

ASSUMPTION: A high richness, high diversity and low AMBI index indicate a high status without perturbation.

As we do not historical data or other reference systems to produce relevant reference conditions, it is difficult to calculate a global M-AMBI here.

No clear tendancy can be discovered here, apart from the fact that the overall status seems to be ‘High’. Several hypothesises can explain this result:

  • the M-AMBI index describes reality well, so that overall perturbation from organic matter is low
  • there is a bias in the index due to the species classification in groups, originally suited for European ecosystems
  • the assumptions for the reference values are not correct
  • the configuration of the bay makes the perturbation small relative to the water volume and bathymetric condition

Further work is needed to determine the individual responses of somes species, along with the use of different methods to understand other perturbations and cumulative impacts.

3.4.3. BENTIX

BENTIX is an index based on the same theory as the AMBI, where species are placed in groups based on their tolerance to perturbation (Simboura & Zenetos, 2002). Here also, this perturbation is principally linked to organic matter increase, but two groups only are present:

  • GS: species that are sensitive or indifferent to a perturbation (~ AMBI groups I and II)
  • GT: species that are tolerant to a perturbation and opportunists (~ AMBI groups III to V)

BENTIX is continuous between 2 and 6 (0 when the habitat is azoic, thus considered extremely disturbed), and is calculated using this equation:

\[ BENTIX = \frac{(6 . P_{GS}) + (2 . P_{GT})}{100} \]

  • \(P_{GS}\) is the proportion of sensitive species (percentage of the total density of species)
  • \(P_{GT}\) is the proportion of tolerant species (percentage of the total density of species)

ASSUMPTION: Sensitive species are only present in pristine ecosystems, while the dominance of opportunists indicates a perturbed state.

Here is the classification of the sampled taxa in ecological groups, adapted from Simboura & Zenetos (2002):

taxon_name group source confidence_score
aceroides_aceroides_latipes S AMBI list 3
akanthophoreus_gracilis S AMBI list 3
ameritella_agilis S AMBI list 3
ameroculodes_edwardsi S AMBI list 3
ampelisca_vadorum S AMBI list 3
amphipoda not_assigned 0
anonyx_lilljeborgi S AMBI list 3
anthozoa S AMBI list 1
arcteobia_anticostiensis S AMBI list (POLYNOIDAE) 2
arrhoges_occidentalis S AMBI list (Aporrhais sp.) 2
astarte_sp S AMBI list 2
axinopsida_orbiculata T AMBI list 3
axiothella_catenata S AMBI list (Axiothella sp.) 2
bathymedon_longimanus S AMBI list 3
bathymedon_obtusifrons S AMBI list 3
bipalponephtys_neotena S AMBI list 3
boreochiton_ruber S AMBI list 3
brachydiastylis_sp S AMBI list 2
byblis_gaimardii S AMBI list 3
cancer_irroratus S Gittenberg & Van Loon, 2013 (Cancer pagurus) 1
caprella_septentrionalis S AMBI list 3
chaetodermatida not_assigned 0
chionoecetes_opilio S AMBI list 3
chlamys_islandica S AMBI list 3
chone_sp S AMBI list 2
ciliatocardium_ciliatum S AMBI list 3
cirripedia S AMBI list 3
cistenides_granulata S AMBI list 3
cossura_longocirrata T AMBI list 3
crassicorophium_bonellii T AMBI list 3
crenella_decussata S AMBI list 3
cumacea S AMBI list 3
cyclocardia_borealis S AMBI list (Cyclocardia thouarsii) 2
cyrtodaria_siliqua S Gilkinson et al., 2005 2
diastylis_rathkei T AMBI list 3
diastylis_sculpta S AMBI list 3
diastylis_sp S AMBI list 1
echinarachnius_parma S AMBI list (ECHINOIDEA) 2
edotia_montosa S AMBI list 3
ennucula_tenuis S AMBI list 3
eteone_sp T AMBI list 2
euchone_sp S AMBI list 2
eudorella_emarginata S AMBI list 3
eudorellopsis_integra S Tillin & Tyler-Walters, 2014 (group of Bathyporeia elegans & Eudorellopsis deformis) 2
euspira_pallida S AMBI list 3
glycera_capitata S AMBI list 3
glycera_sp S AMBI list 2
goniada_maculata S AMBI list 3
guernea_prinassus_nordenskioldi T de la Ossa Carretero et al., 2011 (Dexamene spinosa) 1
halacaridae_spp S AMBI list 2
haminoea_solitaria S AMBI list 3
hardametopa_carinata S AMBI list (STENOTHOIDAE) 1
harmothoe_sp S AMBI list 2
harpacticoida not_assigned 0
hediste_diversicolor T AMBI list 3
heteranomia_squamula S AMBI list 3
hiatella_arctica S AMBI list 3
holothuroidea S AMBI list 3
idotea_phosphorea S AMBI list (Idotea sp.) 2
ischyroceridae_spp S AMBI list (Ischyrocerus anguipes) 2
ischyrocerus_anguipes S AMBI list 3
isopoda not_assigned 0
lacuna_vincta S AMBI list 3
lamprops_fuscatus S AMBI list 3
lamprops_quadriplicata S AMBI list 3
lepeta_caeca S AMBI list 3
leucon_leucon_nasicoides S AMBI list 3
littorina_littorea S AMBI list 3
lumbrineridae_spp S AMBI list 2
lysianassidae_spp S AMBI list 2
macoma_calcarea S AMBI list 3
maera_danae S AMBI list (Maera sp.) 2
maldane_sarsi S AMBI list 3
maldanidae_spp S AMBI list 2
monoculopsis_longicornis S AMBI list 3
muculus_musculus_discors S AMBI list 3
mytilus_sp T AMBI list 2
nematoda T AMBI list 1
nemertea T AMBI list 1
neoleanira_tetragona S AMBI list 3
nephtyidae_spp S AMBI list 2
nephtys_caeca S AMBI list 3
nephtys_incisa S AMBI list 3
nephtys_sp S AMBI list 2
nuculana_minuta S AMBI list 3
nymphonidae_spp not_assigned 0
oenopota_sp S AMBI list 2
oligochaeta T AMBI list 1
ophelia_limacina S AMBI list 3
opheliidae_spp S AMBI list (Ophelia limacina) 2
ophiopholis_aculeata S AMBI list 3
ophiura_robusta S AMBI list 3
orchomenella_minuta S AMBI list 3
ostracoda S Bodegart et al., 1997 ; Ruiz et al., 2005 ; Gooday et al., 2009 1
pagurus_pubescens S AMBI list 3
pagurus_sp S AMBI list 2
pandalus_montagui S AMBI list 3
parathyasira_equalis T AMBI list 3
parvicardium_pinnulatum S AMBI list 3
periploma_leanum S AMBI list (Periploma discus) 2
philine_lima S AMBI list 3
philomedes_sp S AMBI list 3
pholoe_longa S AMBI list (Pholoe sp.) 2
pholoe_sp S AMBI list 2
phoxocephalus_holbolli S AMBI list 3
polynoidae_spp S AMBI list (POLYNOIDAE) 2
pontogeneia_inermis S AMBI list (Pontogeneia rostrata) 2
pontoporeia_femorata S AMBI list 3
praxillella_praetermissa T AMBI list 3
propebela_turricula S AMBI list 3
protomedeia_fasciata S AMBI list 3
protomedeia_grandimana S AMBI list 3
puncturella_noachina S AMBI list 3
quasimelita_formosa S AMBI list (MELITIDAE) 2
quasimelita_quadrispinosa S AMBI list 3
retusa_obtusa S AMBI list 3
sabellidae_spp S AMBI list 2
scoletoma_fragilis S AMBI list 3
scoletoma_sp S AMBI list 2
scoloplos_sp S AMBI list 2
serripes_groenlandicus S AMBI list 3
sipuncula S AMBI list 1
solamen_glandula S AMBI list (Solamen columbianum) 2
solariella_sp S AMBI list 2
strongylocentrotus_sp S AMBI list (Strongylocentrotus droebachiensis) 3
tachyrhynchus_erosus S AMBI list (Turritella sp.) 2
thracia_septentrionalis S AMBI list 3
thyasira_gouldi S AMBI list 3
thyasira_sp S AMBI list 1
trichotropis_bicarinata S AMBI list (Euspira sp.) 2
turritellopsis_stimpsoni S AMBI list (Turritella sp.) 2
yoldia_myalis S AMBI list (Yoldia limatula) 2

When we considered the data without a distinction by station, the global BENTIX index is 5.213659.

4. Ecological Quality Status

When relevant, we calculated an Ecological Quality Ratio (EQR) as established by the WFD and MSFD (which varies between 0 and 1). This ratio is calculated with the folowwing equation:

\[ EQR = \frac{V_{ind} - Ref_{bad}}{Ref_{good} - Ref_{bad}} \]

  • \(V_{ind}\) is the value of an indicator at a certain location
  • \(Ref_{bad}\) is the reference value for a “bad” status
  • \(Ref_{good}\) is the reference value for a “good” status

This ratio is then classed into Ecological Quality Status categories, where reference values and limits for class transitions are specific to each indicator. Five classes are typically described:

  • bad (red #FF0000)
  • poor (orange #FFA500)
  • moderate (yellow #EEEE00)
  • good (green #228B22)
  • high (blue #0000EE)

We calculated this ratio using different indicators, in order to compare their efficiency and relevance.

Specific richness

We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal specific richness.

Shannon index

We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal Shannon index.

Margalef index

We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal Margalef index.

Simpson index

We defined class thresholds with 20 %, 40 %, 60 and 80 % of the maximal Simpson index.

BOPA

We defined class thresholds using the method from Dauvin & Ruellet (2007).

AMBI

We defined class thresholds using the methods from Borja et al. (2000) and Muxika et al. (2005).

M-AMBI

We defined class thresholds using the method from Muxika et al. (2007).

BENTIX

We defined class thresholds using the method from Simboura & Zenetos (2002).

5. Relationships between indicators and abiotic parameters

In this section, we study the statistical relationships between indicators calculated above and different abiotic parameters, in order to understand how well they can be used to detect perturbations.

5.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.

Specific richness

Total density

Total biomass

Shannon index

Margalef index

Simpson index

Pielou evenness

W statistic

Taxonomic diversity

Functional richness

Functional evenness

Functional divergence

AMBI

M_AMBI

BOPA

BENTIX

5.2. Correlation

Correlations have been calculated with Spearman’s rank coefficients.

Correlation coefficients between habitat parameters and indices
  S N B H margalef lambda J W delta FR FE FD AMBI M_AMBI BOPA BENTIX
om -0.026 -0.121 0.012 0.122 0.037 0.133 0.136 0.065 0.059 -0.109 -0.034 0.066 -0.167 0.094 0.184 0.305
gravel 0.029 0.007 0.154 0.012 0.025 0.039 0.07 0.139 0.074 0.242 0.079 -0.139 0.054 -0.005 -0.017 -0.17
sand 0.059 0.084 -0.061 0.031 0.031 0.027 -0.027 0.039 0.081 0.08 0.049 0.075 0.199 -0.018 -0.28 -0.305
silt -0.054 -0.013 0.026 -0.068 -0.057 -0.086 -0.055 -0.088 -0.139 -0.117 -0.091 0.002 -0.17 0.007 0.301 0.279
clay -0.098 -0.076 -0.02 -0.044 -0.086 -0.027 0.02 -0.037 -0.059 0.028 -0.067 -0.08 -0.05 -0.06 0.07 0.02
arsenic -0.266 -0.149 -0.165 -0.193 -0.231 -0.149 0.008 -0.203 -0.125 -0.275 -0.137 0.017 -0.036 -0.196 0.266 0.136
cadmium -0.308 -0.042 -0.173 -0.291 -0.322 -0.252 -0.133 -0.339 -0.275 -0.294 -0.246 0.163 -0.019 -0.279 0.237 0.084
chromium -0.331 -0.167 -0.14 -0.273 -0.311 -0.228 -0.041 -0.242 -0.222 -0.353 -0.173 0.056 -0.021 -0.283 0.287 0.163
copper -0.298 -0.172 -0.145 -0.225 -0.276 -0.187 -0.025 -0.194 -0.199 -0.336 -0.171 0.109 -0.018 -0.255 0.252 0.183
iron -0.377 -0.273 -0.047 -0.251 -0.322 -0.199 0.034 -0.176 -0.171 -0.385 -0.112 0.057 0.004 -0.303 0.248 0.09
manganese -0.287 -0.096 -0.084 -0.261 -0.3 -0.227 -0.085 -0.241 -0.245 -0.314 -0.23 0.069 -0.031 -0.252 0.333 0.162
mercury -0.234 -0.084 -0.016 -0.199 -0.237 -0.173 -0.075 -0.165 -0.228 -0.307 -0.194 0.145 -0.043 -0.184 0.269 0.169
lead -0.304 -0.135 -0.155 -0.252 -0.29 -0.213 -0.051 -0.24 -0.216 -0.312 -0.197 0.097 0.007 -0.264 0.301 0.124
zinc -0.32 -0.145 -0.165 -0.253 -0.303 -0.212 -0.056 -0.252 -0.221 -0.336 -0.188 0.161 -0.01 -0.274 0.26 0.15
p-values of correlation test between habitat parameters and indices
  S N B H margalef lambda J W delta FR FE FD AMBI M_AMBI BOPA BENTIX
om 0.7858 0.2113 0.9048 0.2093 0.7012 0.1701 0.1614 0.5035 0.544 0.2628 0.7284 0.4963 0.08394 0.3319 0.05618 0.001327
gravel 0.7662 0.9425 0.1117 0.9048 0.7948 0.6896 0.4692 0.1512 0.4459 0.01174 0.4148 0.1513 0.5795 0.9576 0.8576 0.07776
sand 0.5414 0.3846 0.5339 0.752 0.7485 0.7805 0.7828 0.6851 0.4045 0.4099 0.6168 0.4429 0.03891 0.8558 0.00334 0.001331
silt 0.581 0.8963 0.7923 0.4834 0.5607 0.3744 0.5692 0.3672 0.1509 0.2264 0.3508 0.9844 0.07875 0.9425 0.001537 0.003456
clay 0.3134 0.4336 0.8338 0.6486 0.3749 0.7849 0.8392 0.7052 0.5453 0.7737 0.4939 0.4117 0.6054 0.5407 0.4685 0.8405
arsenic 0.005376 0.1238 0.08845 0.04503 0.01607 0.1243 0.9356 0.03544 0.1991 0.003949 0.1586 0.8603 0.7147 0.04233 0.005411 0.1592
cadmium 0.001171 0.6656 0.07386 0.002263 0.0006803 0.008567 0.1701 0.0003388 0.00396 0.002036 0.01018 0.09262 0.8475 0.003504 0.01348 0.3893
chromium 0.0004702 0.08459 0.1476 0.004269 0.001052 0.01769 0.6766 0.01158 0.02095 0.0001797 0.07347 0.5643 0.8322 0.003027 0.002598 0.09147
copper 0.001731 0.07495 0.1337 0.0195 0.003892 0.05222 0.8009 0.04442 0.03895 0.0003739 0.07738 0.2608 0.8572 0.007774 0.008463 0.05769
iron 5.82e-05 0.004321 0.6322 0.00892 0.0006646 0.03854 0.725 0.06932 0.07715 3.87e-05 0.2485 0.5548 0.9679 0.001522 0.009583 0.3544
manganese 0.00256 0.3224 0.3861 0.006285 0.001592 0.01793 0.3814 0.01201 0.01088 0.0009191 0.01642 0.4765 0.7532 0.008646 0.0004345 0.09382
mercury 0.0146 0.3891 0.8673 0.03882 0.0135 0.07408 0.4404 0.08806 0.01764 0.001231 0.04375 0.1336 0.6622 0.05723 0.004795 0.07972
lead 0.001371 0.1643 0.109 0.00859 0.002352 0.02692 0.5997 0.01223 0.02485 0.00101 0.04112 0.3171 0.9397 0.005709 0.001546 0.2018
zinc 0.0007423 0.1333 0.08864 0.00819 0.001442 0.02772 0.5628 0.008563 0.02152 0.0003788 0.0519 0.09615 0.9183 0.004098 0.00654 0.1206


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